# Deep Learning-Based Visual Fatigue Detection Using Eye Gaze Patterns in VR

arXiv: https://arxiv.org/abs/2510.12994 · HTML: https://mlsanity.com/paper/2510.12994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Interpretable 3D Human Action Analysis with Temporal Convolutional Networks](https://mlsanity.com/paper/1704.04516/) (2017)
- [GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality](https://mlsanity.com/paper/2210.07533/) (2022)
- [Digital Transformations of Classrooms in Virtual Reality](https://mlsanity.com/paper/2101.09576/) (2021)
- [Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics](https://mlsanity.com/paper/2201.02110/) (2022)
- [Evaluating Deep Networks for Detecting User Familiarity with VR from Hand Interactions](https://mlsanity.com/paper/2401.16443/) (2024)
- [An Analysis of Physiological and Psychological Responses in Virtual Reality and Flat Screen Gaming](https://mlsanity.com/paper/2306.09690/) (2023)
- [Mind the Visual Discomfort: Assessing Event-Related Potentials as Indicators for Visual Strain in Head-Mounted Displays](https://mlsanity.com/paper/2407.18548/) (2024)

## Abstract (arXiv)

Prolonged exposure to virtual reality (VR) systems leads to visual fatigue, impairs user comfort, performance, and safety, particularly in high-stakes or long-duration applications. Existing fatigue detection approaches rely on subjective questionnaires or intrusive physiological signals, such as EEG, heart rate, or eye-blink count, which limit their scalability and real-time applicability. This paper introduces a deep learning-based study for detecting visual fatigue using continuous eye-gaze trajectories recorded in VR. We use the GazeBaseVR dataset comprising binocular eye-tracking data from 407 participants across five immersive tasks, extract cyclopean eye-gaze angles, and evaluate six deep classifiers. Our results demonstrate that EKYT achieves up to 94% accuracy, particularly in tasks demanding high visual attention, such as video viewing and text reading. We further analyze gaze variance and subjective fatigue measures, indicating significant behavioral differences between fatigued and non-fatigued conditions. These findings establish eye-gaze dynamics as a reliable and nonintrusive modality for continuous fatigue detection in immersive VR, offering practical implications for adaptive human-computer interactions.